
Most current standard FL [1]}, [2]}, [3]} and decentralized FL frameworks [4]} still rely on a single edge server to coordinate model aggregations. Single-point-of-failure bottlenecks may disrupt the entire FL system if the server is attacked. Only the work in [5]} has considered a multi-server edge computing model for BFL, but its model aggregation still follows traditional FL.

End-to-end latency optimization, i.e., for both model training and block mining, remains understudied in current BFL systems [6]}, [7]}, [5]}. These works mostly aim to characterize, rather than optimize, BFL latency.

The potential benefits of edge computing have not been well exploited in existing BFL schemes. Most works [9]}, [10]}, [11]} have not considered practical resource constraints of mobile IoT devices and the potential for edge servers to mitigate resulting straggler effects. Only [12]}, [3]} have considered such a scenario, with the focus instead on partial offloading for centralized FL.

None of the existing works have analyzed the convergence properties of BFL in a multi-server system. A comprehensive theoretical analysis will provide insights into BFL operations, leading to potential optimization techniques.

